{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "import numpy as np\n",
    "import requests\n",
    "import getpass\n",
    "import socket\n",
    "import json\n",
    "import zipfile\n",
    "import io\n",
    "import math\n",
    "from glob import glob\n",
    "import os\n",
    "import shutil\n",
    "import pprint\n",
    "import time\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import fiona\n",
    "import re\n",
    "import sys\n",
    "module_path = os.path.abspath(os.path.join('..'))\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)\n",
    "# To read KML files with geopandas, we will need to enable KML support in fiona (disabled by default)\n",
    "fiona.drvsupport.supported_drivers['LIBKML'] = 'rw'\n",
    "from shapely.geometry import Polygon, mapping\n",
    "from shapely.geometry.polygon import orient\n",
    "from statistics import mean\n",
    "from requests.auth import HTTPBasicAuth\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/                        Group\n",
      "/METADATA                Group\n",
      "/METADATA/AcquisitionInformation Group\n",
      "/METADATA/AcquisitionInformation/lidar Group\n",
      "/METADATA/AcquisitionInformation/lidarDocument Group\n",
      "/METADATA/AcquisitionInformation/platform Group\n",
      "/METADATA/AcquisitionInformation/platformDocument Group\n",
      "/METADATA/DataQuality    Group\n",
      "/METADATA/DataQuality/CompletenessOmission Group\n",
      "/METADATA/DataQuality/DomainConsistency Group\n",
      "/METADATA/DatasetIdentification Group\n",
      "/METADATA/Extent         Group\n",
      "/METADATA/Lineage        Group\n",
      "/METADATA/Lineage/ANC06-01 Group\n",
      "/METADATA/Lineage/ANC06-02 Group\n",
      "/METADATA/Lineage/ANC06-03 Group\n",
      "/METADATA/Lineage/ANC17  Group\n",
      "/METADATA/Lineage/ANC19  Group\n",
      "/METADATA/Lineage/ANC25-06 Group\n",
      "/METADATA/Lineage/ANC26-06 Group\n",
      "/METADATA/Lineage/ANC28  Group\n",
      "/METADATA/Lineage/ANC36-06 Group\n",
      "/METADATA/Lineage/ANC38-06 Group\n",
      "/METADATA/Lineage/ATL03  Group\n",
      "/METADATA/Lineage/ATL09  Group\n",
      "/METADATA/Lineage/Control Group\n",
      "/METADATA/ProcessStep    Group\n",
      "/METADATA/ProcessStep/Browse Group\n",
      "/METADATA/ProcessStep/Metadata Group\n",
      "/METADATA/ProcessStep/PGE Group\n",
      "/METADATA/ProcessStep/QA Group\n",
      "/METADATA/ProductSpecificationDocument Group\n",
      "/METADATA/QADatasetIdentification Group\n",
      "/METADATA/SeriesIdentification Group\n",
      "/ancillary_data          Group\n",
      "/ancillary_data/atlas_sdp_gps_epoch Dataset {1}\n",
      "/ancillary_data/control  Dataset {1}\n",
      "/ancillary_data/data_end_utc Dataset {1}\n",
      "/ancillary_data/data_start_utc Dataset {1}\n",
      "/ancillary_data/end_cycle Dataset {1}\n",
      "/ancillary_data/end_delta_time Dataset {1}\n",
      "/ancillary_data/end_geoseg Dataset {1}\n",
      "/ancillary_data/end_gpssow Dataset {1}\n",
      "/ancillary_data/end_gpsweek Dataset {1}\n",
      "/ancillary_data/end_orbit Dataset {1}\n",
      "/ancillary_data/end_region Dataset {1}\n",
      "/ancillary_data/end_rgt  Dataset {1}\n",
      "/ancillary_data/granule_end_utc Dataset {1}\n",
      "/ancillary_data/granule_start_utc Dataset {1}\n",
      "/ancillary_data/land_ice Group\n",
      "/ancillary_data/land_ice/dt_hist Dataset {1}\n",
      "/ancillary_data/land_ice/fit_maxiter Dataset {1}\n",
      "/ancillary_data/land_ice/fpb_maxiter Dataset {1}\n",
      "/ancillary_data/land_ice/max_res_ids Dataset {1}\n",
      "/ancillary_data/land_ice/maxiter Dataset {1}\n",
      "/ancillary_data/land_ice/min_dist Dataset {1}\n",
      "/ancillary_data/land_ice/min_gain_th Dataset {1}\n",
      "/ancillary_data/land_ice/min_n_pe Dataset {1}\n",
      "/ancillary_data/land_ice/min_n_sel Dataset {1}\n",
      "/ancillary_data/land_ice/min_signal_conf Dataset {1}\n",
      "/ancillary_data/land_ice/n_hist Dataset {1}\n",
      "/ancillary_data/land_ice/n_sigmas Dataset {1}\n",
      "/ancillary_data/land_ice/nhist_bins Dataset {1}\n",
      "/ancillary_data/land_ice/proc_interval Dataset {1}\n",
      "/ancillary_data/land_ice/rbin_width Dataset {1}\n",
      "/ancillary_data/land_ice/sigma_beam Dataset {1}\n",
      "/ancillary_data/land_ice/sigma_tx Dataset {1}\n",
      "/ancillary_data/land_ice/t_dead Dataset {1}\n",
      "/ancillary_data/land_ice/win_nsig Dataset {2}\n",
      "/ancillary_data/qa_at_interval Dataset {1}\n",
      "/ancillary_data/release  Dataset {1}\n",
      "/ancillary_data/start_cycle Dataset {1}\n",
      "/ancillary_data/start_delta_time Dataset {1}\n",
      "/ancillary_data/start_geoseg Dataset {1}\n",
      "/ancillary_data/start_gpssow Dataset {1}\n",
      "/ancillary_data/start_gpsweek Dataset {1}\n",
      "/ancillary_data/start_orbit Dataset {1}\n",
      "/ancillary_data/start_region Dataset {1}\n",
      "/ancillary_data/start_rgt Dataset {1}\n",
      "/ancillary_data/version  Dataset {1}\n",
      "/gt1l                    Group\n",
      "/gt1l/land_ice_segments  Group\n",
      "/gt1l/land_ice_segments/atl06_quality_summary Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/bias_correction Group\n",
      "/gt1l/land_ice_segments/bias_correction/fpb_mean_corr Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/bias_correction/fpb_mean_corr_sigma Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/bias_correction/fpb_med_corr Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/bias_correction/fpb_med_corr_sigma Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/bias_correction/fpb_n_corr Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/bias_correction/med_r_fit Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/bias_correction/tx_mean_corr Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/bias_correction/tx_med_corr Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/delta_time Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/dem Group\n",
      "/gt1l/land_ice_segments/dem/dem_flag Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/dem/dem_h Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/dem/geoid_h Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics Group\n",
      "/gt1l/land_ice_segments/fit_statistics/dh_fit_dx Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/dh_fit_dx_sigma Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/dh_fit_dy Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/h_expected_rms Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/h_mean Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/h_rms_misfit Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/h_robust_sprd Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/n_fit_photons Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/n_seg_pulses Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/sigma_h_mean Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/signal_selection_source Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/signal_selection_source_status Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/snr Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/snr_significance Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/fit_statistics/w_surface_window_final Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/geophysical Group\n",
      "/gt1l/land_ice_segments/geophysical/bckgrd Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/bsnow_conf Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/bsnow_h Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/bsnow_od Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/cloud_flg_asr Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/cloud_flg_atm Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/dac Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/e_bckgrd Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/msw_flag Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/neutat_delay_total Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/r_eff Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/solar_azimuth Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/solar_elevation Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/tide_earth Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/tide_load Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/tide_ocean Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/geophysical/tide_pole Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/ground_track Group\n",
      "/gt1l/land_ice_segments/ground_track/ref_azimuth Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/ground_track/ref_coelv Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/ground_track/seg_azimuth Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/ground_track/sigma_geo_at Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/ground_track/sigma_geo_xt Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/ground_track/x_atc Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/ground_track/y_atc Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/h_li Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/h_li_sigma Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/latitude Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/longitude Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/segment_id Dataset {65318/Inf}\n",
      "/gt1l/land_ice_segments/sigma_geo_h Dataset {65318/Inf}\n",
      "/gt1l/residual_histogram Group\n",
      "/gt1l/residual_histogram/bckgrd_per_bin Dataset {6526/Inf}\n",
      "/gt1l/residual_histogram/count Dataset {6526/Inf, 1000}\n",
      "/gt1l/residual_histogram/delta_time Dataset {6526/Inf}\n",
      "/gt1l/residual_histogram/dh Dataset {1000}\n",
      "/gt1l/residual_histogram/ds_segment_id Dataset {10}\n",
      "/gt1l/residual_histogram/lat_mean Dataset {6526/Inf}\n",
      "/gt1l/residual_histogram/lon_mean Dataset {6526/Inf}\n",
      "/gt1l/residual_histogram/pulse_count Dataset {6526/Inf}\n",
      "/gt1l/residual_histogram/segment_id_list Dataset {6526/Inf, 10}\n",
      "/gt1l/residual_histogram/x_atc_mean Dataset {6526/Inf}\n",
      "/gt1l/segment_quality    Group\n",
      "/gt1l/segment_quality/delta_time Dataset {66192/Inf}\n",
      "/gt1l/segment_quality/record_number Dataset {66192/Inf}\n",
      "/gt1l/segment_quality/reference_pt_lat Dataset {66192/Inf}\n",
      "/gt1l/segment_quality/reference_pt_lon Dataset {66192/Inf}\n",
      "/gt1l/segment_quality/segment_id Dataset {66192/Inf}\n",
      "/gt1l/segment_quality/signal_selection_source Dataset {66192/Inf}\n",
      "/gt1l/segment_quality/signal_selection_status Group\n",
      "/gt1l/segment_quality/signal_selection_status/signal_selection_status_all Dataset {66192/Inf}\n",
      "/gt1l/segment_quality/signal_selection_status/signal_selection_status_backup Dataset {66192/Inf}\n",
      "/gt1l/segment_quality/signal_selection_status/signal_selection_status_confident Dataset {66192/Inf}\n",
      "/gt1r                    Group\n",
      "/gt1r/land_ice_segments  Group\n",
      "/gt1r/land_ice_segments/atl06_quality_summary Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/bias_correction Group\n",
      "/gt1r/land_ice_segments/bias_correction/fpb_mean_corr Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/bias_correction/fpb_mean_corr_sigma Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/bias_correction/fpb_med_corr Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/bias_correction/fpb_med_corr_sigma Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/bias_correction/fpb_n_corr Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/bias_correction/med_r_fit Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/bias_correction/tx_mean_corr Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/bias_correction/tx_med_corr Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/delta_time Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/dem Group\n",
      "/gt1r/land_ice_segments/dem/dem_flag Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/dem/dem_h Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/dem/geoid_h Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics Group\n",
      "/gt1r/land_ice_segments/fit_statistics/dh_fit_dx Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/dh_fit_dx_sigma Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/dh_fit_dy Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/h_expected_rms Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/h_mean Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/h_rms_misfit Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/h_robust_sprd Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/n_fit_photons Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/n_seg_pulses Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/sigma_h_mean Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/signal_selection_source Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/signal_selection_source_status Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/snr Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/snr_significance Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/fit_statistics/w_surface_window_final Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/geophysical Group\n",
      "/gt1r/land_ice_segments/geophysical/bckgrd Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/bsnow_conf Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/bsnow_h Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/bsnow_od Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/cloud_flg_asr Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/cloud_flg_atm Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/dac Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/e_bckgrd Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/msw_flag Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/neutat_delay_total Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/r_eff Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/solar_azimuth Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/solar_elevation Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/tide_earth Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/tide_load Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/tide_ocean Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/geophysical/tide_pole Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/ground_track Group\n",
      "/gt1r/land_ice_segments/ground_track/ref_azimuth Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/ground_track/ref_coelv Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/ground_track/seg_azimuth Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/ground_track/sigma_geo_at Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/ground_track/sigma_geo_xt Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/ground_track/x_atc Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/ground_track/y_atc Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/h_li Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/h_li_sigma Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/latitude Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/longitude Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/segment_id Dataset {65318/Inf}\n",
      "/gt1r/land_ice_segments/sigma_geo_h Dataset {65318/Inf}\n",
      "/gt1r/residual_histogram Group\n",
      "/gt1r/residual_histogram/bckgrd_per_bin Dataset {6526/Inf}\n",
      "/gt1r/residual_histogram/count Dataset {6526/Inf, 1000}\n",
      "/gt1r/residual_histogram/delta_time Dataset {6526/Inf}\n",
      "/gt1r/residual_histogram/dh Dataset {1000}\n",
      "/gt1r/residual_histogram/ds_segment_id Dataset {10}\n",
      "/gt1r/residual_histogram/lat_mean Dataset {6526/Inf}\n",
      "/gt1r/residual_histogram/lon_mean Dataset {6526/Inf}\n",
      "/gt1r/residual_histogram/pulse_count Dataset {6526/Inf}\n",
      "/gt1r/residual_histogram/segment_id_list Dataset {6526/Inf, 10}\n",
      "/gt1r/residual_histogram/x_atc_mean Dataset {6526/Inf}\n",
      "/gt1r/segment_quality    Group\n",
      "/gt1r/segment_quality/delta_time Dataset {66192/Inf}\n",
      "/gt1r/segment_quality/record_number Dataset {66192/Inf}\n",
      "/gt1r/segment_quality/reference_pt_lat Dataset {66192/Inf}\n",
      "/gt1r/segment_quality/reference_pt_lon Dataset {66192/Inf}\n",
      "/gt1r/segment_quality/segment_id Dataset {66192/Inf}\n",
      "/gt1r/segment_quality/signal_selection_source Dataset {66192/Inf}\n",
      "/gt1r/segment_quality/signal_selection_status Group\n",
      "/gt1r/segment_quality/signal_selection_status/signal_selection_status_all Dataset {66192/Inf}\n",
      "/gt1r/segment_quality/signal_selection_status/signal_selection_status_backup Dataset {66192/Inf}\n",
      "/gt1r/segment_quality/signal_selection_status/signal_selection_status_confident Dataset {66192/Inf}\n",
      "/gt2l                    Group\n",
      "/gt2l/land_ice_segments  Group\n",
      "/gt2l/land_ice_segments/atl06_quality_summary Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/bias_correction Group\n",
      "/gt2l/land_ice_segments/bias_correction/fpb_mean_corr Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/bias_correction/fpb_mean_corr_sigma Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/bias_correction/fpb_med_corr Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/bias_correction/fpb_med_corr_sigma Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/bias_correction/fpb_n_corr Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/bias_correction/med_r_fit Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/bias_correction/tx_mean_corr Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/bias_correction/tx_med_corr Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/delta_time Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/dem Group\n",
      "/gt2l/land_ice_segments/dem/dem_flag Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/dem/dem_h Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/dem/geoid_h Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics Group\n",
      "/gt2l/land_ice_segments/fit_statistics/dh_fit_dx Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/dh_fit_dx_sigma Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/dh_fit_dy Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/h_expected_rms Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/h_mean Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/h_rms_misfit Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/h_robust_sprd Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/n_fit_photons Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/n_seg_pulses Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/sigma_h_mean Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/signal_selection_source Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/signal_selection_source_status Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/snr Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/snr_significance Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/fit_statistics/w_surface_window_final Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/geophysical Group\n",
      "/gt2l/land_ice_segments/geophysical/bckgrd Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/bsnow_conf Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/bsnow_h Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/bsnow_od Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/cloud_flg_asr Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/cloud_flg_atm Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/dac Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/e_bckgrd Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/msw_flag Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/neutat_delay_total Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/r_eff Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/solar_azimuth Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/solar_elevation Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/tide_earth Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/tide_load Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/tide_ocean Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/geophysical/tide_pole Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/ground_track Group\n",
      "/gt2l/land_ice_segments/ground_track/ref_azimuth Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/ground_track/ref_coelv Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/ground_track/seg_azimuth Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/ground_track/sigma_geo_at Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/ground_track/sigma_geo_xt Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/ground_track/x_atc Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/ground_track/y_atc Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/h_li Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/h_li_sigma Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/latitude Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/longitude Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/segment_id Dataset {65123/Inf}\n",
      "/gt2l/land_ice_segments/sigma_geo_h Dataset {65123/Inf}\n",
      "/gt2l/residual_histogram Group\n",
      "/gt2l/residual_histogram/bckgrd_per_bin Dataset {6490/Inf}\n",
      "/gt2l/residual_histogram/count Dataset {6490/Inf, 1000}\n",
      "/gt2l/residual_histogram/delta_time Dataset {6490/Inf}\n",
      "/gt2l/residual_histogram/dh Dataset {1000}\n",
      "/gt2l/residual_histogram/ds_segment_id Dataset {10}\n",
      "/gt2l/residual_histogram/lat_mean Dataset {6490/Inf}\n",
      "/gt2l/residual_histogram/lon_mean Dataset {6490/Inf}\n",
      "/gt2l/residual_histogram/pulse_count Dataset {6490/Inf}\n",
      "/gt2l/residual_histogram/segment_id_list Dataset {6490/Inf, 10}\n",
      "/gt2l/residual_histogram/x_atc_mean Dataset {6490/Inf}\n",
      "/gt2l/segment_quality    Group\n",
      "/gt2l/segment_quality/delta_time Dataset {66170/Inf}\n",
      "/gt2l/segment_quality/record_number Dataset {66170/Inf}\n",
      "/gt2l/segment_quality/reference_pt_lat Dataset {66170/Inf}\n",
      "/gt2l/segment_quality/reference_pt_lon Dataset {66170/Inf}\n",
      "/gt2l/segment_quality/segment_id Dataset {66170/Inf}\n",
      "/gt2l/segment_quality/signal_selection_source Dataset {66170/Inf}\n",
      "/gt2l/segment_quality/signal_selection_status Group\n",
      "/gt2l/segment_quality/signal_selection_status/signal_selection_status_all Dataset {66170/Inf}\n",
      "/gt2l/segment_quality/signal_selection_status/signal_selection_status_backup Dataset {66170/Inf}\n",
      "/gt2l/segment_quality/signal_selection_status/signal_selection_status_confident Dataset {66170/Inf}\n",
      "/gt2r                    Group\n",
      "/gt2r/land_ice_segments  Group\n",
      "/gt2r/land_ice_segments/atl06_quality_summary Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/bias_correction Group\n",
      "/gt2r/land_ice_segments/bias_correction/fpb_mean_corr Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/bias_correction/fpb_mean_corr_sigma Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/bias_correction/fpb_med_corr Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/bias_correction/fpb_med_corr_sigma Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/bias_correction/fpb_n_corr Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/bias_correction/med_r_fit Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/bias_correction/tx_mean_corr Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/bias_correction/tx_med_corr Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/delta_time Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/dem Group\n",
      "/gt2r/land_ice_segments/dem/dem_flag Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/dem/dem_h Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/dem/geoid_h Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics Group\n",
      "/gt2r/land_ice_segments/fit_statistics/dh_fit_dx Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/dh_fit_dx_sigma Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/dh_fit_dy Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/h_expected_rms Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/h_mean Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/h_rms_misfit Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/h_robust_sprd Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/n_fit_photons Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/n_seg_pulses Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/sigma_h_mean Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/signal_selection_source Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/signal_selection_source_status Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/snr Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/snr_significance Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/fit_statistics/w_surface_window_final Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/geophysical Group\n",
      "/gt2r/land_ice_segments/geophysical/bckgrd Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/bsnow_conf Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/bsnow_h Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/bsnow_od Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/cloud_flg_asr Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/cloud_flg_atm Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/dac Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/e_bckgrd Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/msw_flag Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/neutat_delay_total Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/r_eff Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/solar_azimuth Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/solar_elevation Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/tide_earth Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/tide_load Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/tide_ocean Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/geophysical/tide_pole Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/ground_track Group\n",
      "/gt2r/land_ice_segments/ground_track/ref_azimuth Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/ground_track/ref_coelv Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/ground_track/seg_azimuth Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/ground_track/sigma_geo_at Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/ground_track/sigma_geo_xt Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/ground_track/x_atc Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/ground_track/y_atc Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/h_li Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/h_li_sigma Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/latitude Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/longitude Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/segment_id Dataset {65123/Inf}\n",
      "/gt2r/land_ice_segments/sigma_geo_h Dataset {65123/Inf}\n",
      "/gt2r/residual_histogram Group\n",
      "/gt2r/residual_histogram/bckgrd_per_bin Dataset {6490/Inf}\n",
      "/gt2r/residual_histogram/count Dataset {6490/Inf, 1000}\n",
      "/gt2r/residual_histogram/delta_time Dataset {6490/Inf}\n",
      "/gt2r/residual_histogram/dh Dataset {1000}\n",
      "/gt2r/residual_histogram/ds_segment_id Dataset {10}\n",
      "/gt2r/residual_histogram/lat_mean Dataset {6490/Inf}\n",
      "/gt2r/residual_histogram/lon_mean Dataset {6490/Inf}\n",
      "/gt2r/residual_histogram/pulse_count Dataset {6490/Inf}\n",
      "/gt2r/residual_histogram/segment_id_list Dataset {6490/Inf, 10}\n",
      "/gt2r/residual_histogram/x_atc_mean Dataset {6490/Inf}\n",
      "/gt2r/segment_quality    Group\n",
      "/gt2r/segment_quality/delta_time Dataset {66170/Inf}\n",
      "/gt2r/segment_quality/record_number Dataset {66170/Inf}\n",
      "/gt2r/segment_quality/reference_pt_lat Dataset {66170/Inf}\n",
      "/gt2r/segment_quality/reference_pt_lon Dataset {66170/Inf}\n",
      "/gt2r/segment_quality/segment_id Dataset {66170/Inf}\n",
      "/gt2r/segment_quality/signal_selection_source Dataset {66170/Inf}\n",
      "/gt2r/segment_quality/signal_selection_status Group\n",
      "/gt2r/segment_quality/signal_selection_status/signal_selection_status_all Dataset {66170/Inf}\n",
      "/gt2r/segment_quality/signal_selection_status/signal_selection_status_backup Dataset {66170/Inf}\n",
      "/gt2r/segment_quality/signal_selection_status/signal_selection_status_confident Dataset {66170/Inf}\n",
      "/gt3l                    Group\n",
      "/gt3l/land_ice_segments  Group\n",
      "/gt3l/land_ice_segments/atl06_quality_summary Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/bias_correction Group\n",
      "/gt3l/land_ice_segments/bias_correction/fpb_mean_corr Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/bias_correction/fpb_mean_corr_sigma Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/bias_correction/fpb_med_corr Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/bias_correction/fpb_med_corr_sigma Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/bias_correction/fpb_n_corr Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/bias_correction/med_r_fit Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/bias_correction/tx_mean_corr Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/bias_correction/tx_med_corr Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/delta_time Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/dem Group\n",
      "/gt3l/land_ice_segments/dem/dem_flag Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/dem/dem_h Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/dem/geoid_h Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics Group\n",
      "/gt3l/land_ice_segments/fit_statistics/dh_fit_dx Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/dh_fit_dx_sigma Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/dh_fit_dy Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/h_expected_rms Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/h_mean Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/h_rms_misfit Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/h_robust_sprd Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/n_fit_photons Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/n_seg_pulses Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/sigma_h_mean Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/signal_selection_source Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/signal_selection_source_status Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/snr Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/snr_significance Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/fit_statistics/w_surface_window_final Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/geophysical Group\n",
      "/gt3l/land_ice_segments/geophysical/bckgrd Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/bsnow_conf Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/bsnow_h Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/bsnow_od Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/cloud_flg_asr Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/cloud_flg_atm Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/dac Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/e_bckgrd Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/msw_flag Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/neutat_delay_total Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/r_eff Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/solar_azimuth Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/solar_elevation Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/tide_earth Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/tide_load Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/tide_ocean Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/geophysical/tide_pole Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/ground_track Group\n",
      "/gt3l/land_ice_segments/ground_track/ref_azimuth Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/ground_track/ref_coelv Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/ground_track/seg_azimuth Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/ground_track/sigma_geo_at Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/ground_track/sigma_geo_xt Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/ground_track/x_atc Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/ground_track/y_atc Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/h_li Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/h_li_sigma Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/latitude Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/longitude Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/segment_id Dataset {65781/Inf}\n",
      "/gt3l/land_ice_segments/sigma_geo_h Dataset {65781/Inf}\n",
      "/gt3l/residual_histogram Group\n",
      "/gt3l/residual_histogram/bckgrd_per_bin Dataset {6567/Inf}\n",
      "/gt3l/residual_histogram/count Dataset {6567/Inf, 1000}\n",
      "/gt3l/residual_histogram/delta_time Dataset {6567/Inf}\n",
      "/gt3l/residual_histogram/dh Dataset {1000}\n",
      "/gt3l/residual_histogram/ds_segment_id Dataset {10}\n",
      "/gt3l/residual_histogram/lat_mean Dataset {6567/Inf}\n",
      "/gt3l/residual_histogram/lon_mean Dataset {6567/Inf}\n",
      "/gt3l/residual_histogram/pulse_count Dataset {6567/Inf}\n",
      "/gt3l/residual_histogram/segment_id_list Dataset {6567/Inf, 10}\n",
      "/gt3l/residual_histogram/x_atc_mean Dataset {6567/Inf}\n",
      "/gt3l/segment_quality    Group\n",
      "/gt3l/segment_quality/delta_time Dataset {66148/Inf}\n",
      "/gt3l/segment_quality/record_number Dataset {66148/Inf}\n",
      "/gt3l/segment_quality/reference_pt_lat Dataset {66148/Inf}\n",
      "/gt3l/segment_quality/reference_pt_lon Dataset {66148/Inf}\n",
      "/gt3l/segment_quality/segment_id Dataset {66148/Inf}\n",
      "/gt3l/segment_quality/signal_selection_source Dataset {66148/Inf}\n",
      "/gt3l/segment_quality/signal_selection_status Group\n",
      "/gt3l/segment_quality/signal_selection_status/signal_selection_status_all Dataset {66148/Inf}\n",
      "/gt3l/segment_quality/signal_selection_status/signal_selection_status_backup Dataset {66148/Inf}\n",
      "/gt3l/segment_quality/signal_selection_status/signal_selection_status_confident Dataset {66148/Inf}\n",
      "/gt3r                    Group\n",
      "/gt3r/land_ice_segments  Group\n",
      "/gt3r/land_ice_segments/atl06_quality_summary Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/bias_correction Group\n",
      "/gt3r/land_ice_segments/bias_correction/fpb_mean_corr Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/bias_correction/fpb_mean_corr_sigma Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/bias_correction/fpb_med_corr Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/bias_correction/fpb_med_corr_sigma Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/bias_correction/fpb_n_corr Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/bias_correction/med_r_fit Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/bias_correction/tx_mean_corr Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/bias_correction/tx_med_corr Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/delta_time Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/dem Group\n",
      "/gt3r/land_ice_segments/dem/dem_flag Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/dem/dem_h Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/dem/geoid_h Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics Group\n",
      "/gt3r/land_ice_segments/fit_statistics/dh_fit_dx Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/dh_fit_dx_sigma Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/dh_fit_dy Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/h_expected_rms Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/h_mean Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/h_rms_misfit Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/h_robust_sprd Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/n_fit_photons Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/n_seg_pulses Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/sigma_h_mean Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/signal_selection_source Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/signal_selection_source_status Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/snr Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/snr_significance Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/fit_statistics/w_surface_window_final Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/geophysical Group\n",
      "/gt3r/land_ice_segments/geophysical/bckgrd Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/bsnow_conf Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/bsnow_h Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/bsnow_od Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/cloud_flg_asr Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/cloud_flg_atm Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/dac Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/e_bckgrd Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/msw_flag Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/neutat_delay_total Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/r_eff Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/solar_azimuth Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/solar_elevation Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/tide_earth Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/tide_load Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/tide_ocean Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/geophysical/tide_pole Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/ground_track Group\n",
      "/gt3r/land_ice_segments/ground_track/ref_azimuth Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/ground_track/ref_coelv Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/ground_track/seg_azimuth Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/ground_track/sigma_geo_at Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/ground_track/sigma_geo_xt Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/ground_track/x_atc Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/ground_track/y_atc Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/h_li Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/h_li_sigma Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/latitude Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/longitude Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/segment_id Dataset {65781/Inf}\n",
      "/gt3r/land_ice_segments/sigma_geo_h Dataset {65781/Inf}\n",
      "/gt3r/residual_histogram Group\n",
      "/gt3r/residual_histogram/bckgrd_per_bin Dataset {6567/Inf}\n",
      "/gt3r/residual_histogram/count Dataset {6567/Inf, 1000}\n",
      "/gt3r/residual_histogram/delta_time Dataset {6567/Inf}\n",
      "/gt3r/residual_histogram/dh Dataset {1000}\n",
      "/gt3r/residual_histogram/ds_segment_id Dataset {10}\n",
      "/gt3r/residual_histogram/lat_mean Dataset {6567/Inf}\n",
      "/gt3r/residual_histogram/lon_mean Dataset {6567/Inf}\n",
      "/gt3r/residual_histogram/pulse_count Dataset {6567/Inf}\n",
      "/gt3r/residual_histogram/segment_id_list Dataset {6567/Inf, 10}\n",
      "/gt3r/residual_histogram/x_atc_mean Dataset {6567/Inf}\n",
      "/gt3r/segment_quality    Group\n",
      "/gt3r/segment_quality/delta_time Dataset {66148/Inf}\n",
      "/gt3r/segment_quality/record_number Dataset {66148/Inf}\n",
      "/gt3r/segment_quality/reference_pt_lat Dataset {66148/Inf}\n",
      "/gt3r/segment_quality/reference_pt_lon Dataset {66148/Inf}\n",
      "/gt3r/segment_quality/segment_id Dataset {66148/Inf}\n",
      "/gt3r/segment_quality/signal_selection_source Dataset {66148/Inf}\n",
      "/gt3r/segment_quality/signal_selection_status Group\n",
      "/gt3r/segment_quality/signal_selection_status/signal_selection_status_all Dataset {66148/Inf}\n",
      "/gt3r/segment_quality/signal_selection_status/signal_selection_status_backup Dataset {66148/Inf}\n",
      "/gt3r/segment_quality/signal_selection_status/signal_selection_status_confident Dataset {66148/Inf}\n",
      "/orbit_info              Group\n",
      "/orbit_info/crossing_time Dataset {1/Inf}\n",
      "/orbit_info/cycle_number Dataset {1/Inf}\n",
      "/orbit_info/lan          Dataset {1/Inf}\n",
      "/orbit_info/orbit_number Dataset {1/Inf}\n",
      "/orbit_info/rgt          Dataset {1/Inf}\n",
      "/orbit_info/sc_orient    Dataset {1/Inf}\n",
      "/orbit_info/sc_orient_time Dataset {1/Inf}\n",
      "/quality_assessment      Group\n",
      "/quality_assessment/gt1l Group\n",
      "/quality_assessment/gt1l/delta_time Dataset {133/Inf}\n",
      "/quality_assessment/gt1l/lat_mean Dataset {133/Inf}\n",
      "/quality_assessment/gt1l/lon_mean Dataset {133/Inf}\n",
      "/quality_assessment/gt1l/signal_selection_source_fraction_0 Dataset {133/Inf}\n",
      "/quality_assessment/gt1l/signal_selection_source_fraction_1 Dataset {133/Inf}\n",
      "/quality_assessment/gt1l/signal_selection_source_fraction_2 Dataset {133/Inf}\n",
      "/quality_assessment/gt1l/signal_selection_source_fraction_3 Dataset {133/Inf}\n",
      "/quality_assessment/gt1r Group\n",
      "/quality_assessment/gt1r/delta_time Dataset {133/Inf}\n",
      "/quality_assessment/gt1r/lat_mean Dataset {133/Inf}\n",
      "/quality_assessment/gt1r/lon_mean Dataset {133/Inf}\n",
      "/quality_assessment/gt1r/signal_selection_source_fraction_0 Dataset {133/Inf}\n",
      "/quality_assessment/gt1r/signal_selection_source_fraction_1 Dataset {133/Inf}\n",
      "/quality_assessment/gt1r/signal_selection_source_fraction_2 Dataset {133/Inf}\n",
      "/quality_assessment/gt1r/signal_selection_source_fraction_3 Dataset {133/Inf}\n",
      "/quality_assessment/gt2l Group\n",
      "/quality_assessment/gt2l/delta_time Dataset {133/Inf}\n",
      "/quality_assessment/gt2l/lat_mean Dataset {133/Inf}\n",
      "/quality_assessment/gt2l/lon_mean Dataset {133/Inf}\n",
      "/quality_assessment/gt2l/signal_selection_source_fraction_0 Dataset {133/Inf}\n",
      "/quality_assessment/gt2l/signal_selection_source_fraction_1 Dataset {133/Inf}\n",
      "/quality_assessment/gt2l/signal_selection_source_fraction_2 Dataset {133/Inf}\n",
      "/quality_assessment/gt2l/signal_selection_source_fraction_3 Dataset {133/Inf}\n",
      "/quality_assessment/gt2r Group\n",
      "/quality_assessment/gt2r/delta_time Dataset {133/Inf}\n",
      "/quality_assessment/gt2r/lat_mean Dataset {133/Inf}\n",
      "/quality_assessment/gt2r/lon_mean Dataset {133/Inf}\n",
      "/quality_assessment/gt2r/signal_selection_source_fraction_0 Dataset {133/Inf}\n",
      "/quality_assessment/gt2r/signal_selection_source_fraction_1 Dataset {133/Inf}\n",
      "/quality_assessment/gt2r/signal_selection_source_fraction_2 Dataset {133/Inf}\n",
      "/quality_assessment/gt2r/signal_selection_source_fraction_3 Dataset {133/Inf}\n",
      "/quality_assessment/gt3l Group\n",
      "/quality_assessment/gt3l/delta_time Dataset {133/Inf}\n",
      "/quality_assessment/gt3l/lat_mean Dataset {133/Inf}\n",
      "/quality_assessment/gt3l/lon_mean Dataset {133/Inf}\n",
      "/quality_assessment/gt3l/signal_selection_source_fraction_0 Dataset {133/Inf}\n",
      "/quality_assessment/gt3l/signal_selection_source_fraction_1 Dataset {133/Inf}\n",
      "/quality_assessment/gt3l/signal_selection_source_fraction_2 Dataset {133/Inf}\n",
      "/quality_assessment/gt3l/signal_selection_source_fraction_3 Dataset {133/Inf}\n",
      "/quality_assessment/gt3r Group\n",
      "/quality_assessment/gt3r/delta_time Dataset {133/Inf}\n",
      "/quality_assessment/gt3r/lat_mean Dataset {133/Inf}\n",
      "/quality_assessment/gt3r/lon_mean Dataset {133/Inf}\n",
      "/quality_assessment/gt3r/signal_selection_source_fraction_0 Dataset {133/Inf}\n",
      "/quality_assessment/gt3r/signal_selection_source_fraction_1 Dataset {133/Inf}\n",
      "/quality_assessment/gt3r/signal_selection_source_fraction_2 Dataset {133/Inf}\n",
      "/quality_assessment/gt3r/signal_selection_source_fraction_3 Dataset {133/Inf}\n",
      "/quality_assessment/qa_granule_fail_reason Dataset {1}\n",
      "/quality_assessment/qa_granule_pass_fail Dataset {1}\n"
     ]
    }
   ],
   "source": [
    "# View data file information\n",
    "!h5ls -r '/home/jovyan/ATL06/ATL06_20190102184312_00810210_001_01.h5'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the 'gtxx' folders contain \"bckgrd_atlas' and 'heights' as branches, which contain background counts and geolocated photon information, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grabbing the data, sticking to the center strong laser\n",
    "with h5py.File('/home/jovyan/ATL03/ATL03_20190102184312_00810210_001_01.h5') as f:\n",
    "    elev_atl03 = f['/gt2l/heights/h_ph'][:]\n",
    "    elev_atl03_liex = f['/gt2l/heights/h_ph'][:]\n",
    "    elev_atl03_liex_low = f['/gt2l/heights/h_ph'][:]\n",
    "    lat_atl03 = f['/gt2l/heights/lat_ph'][:]\n",
    "    ph_class = f['/gt2l/heights/signal_conf_ph'][:]\n",
    "    f.close()\n",
    "    \n",
    "with h5py.File('/home/jovyan/ATL06/ATL06_20190102184312_00810210_001_01.h5') as f:\n",
    "    elev_atl06 = f['/gt2l/land_ice_segments/h_li'][:]\n",
    "    lat_atl06 = f['/gt2l/land_ice_segments/latitude'][:]\n",
    "    f.close()\n",
    "    \n",
    "elev_atl06[(elev_atl06 > 10000)] = np.nan\n",
    "elev_atl03_liex[ph_class[:,3]!=4] = np.nan\n",
    "\n",
    "# Filter ATL03 to only include high confidence photons\n",
    "class_consol = [None]*len(elev_atl03)\n",
    "for i in range(0,len(elev_atl03)):\n",
    "    class_consol[i] = max(ph_class[i,0], ph_class[i,1], ph_class[i,2], ph_class[i,3], ph_class[i,4])\n",
    "\n",
    "    if class_consol[i] != 4:\n",
    "        elev_atl03[i] = np.nan\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First Test Case\n",
    "Latitude (x-axis) Bounds: [-72.925, -72.875]\n",
    "Y-Axis Limits: [204.5, 223]\n",
    "\n",
    "## Second Test Case\n",
    "Latitude (x-axis) Bounds: [-73, -72.987]\n",
    "Y-Axis Limits: [217.5, 226.5]\n",
    "\n",
    "## Third Test Case\n",
    "** Is in gt1l, not gt2l! **\n",
    "Latitude (x-axis) Bounds: [-72.63,72.53]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Plotting\n",
    "subset1 = ( (lat_atl03>=-72.925) & (lat_atl03<=-72.875)) # Ideal test case is between [-72.925,-72.875]\n",
    "subset2 = ( (lat_atl06 >= -72.925) & (lat_atl06 <= -72.875) ) # Another good case is [-73, -72.987]\n",
    "ylims = [204.5, 223]\n",
    "\n",
    "%matplotlib widget\n",
    "f1,ax = plt.subplots(1, 2,figsize=(10,6))\n",
    "\n",
    "ax[0].plot(lat_atl03[subset1], elev_atl03[subset1], '.', markersize=1)\n",
    "ax[1].plot(lat_atl06[subset2],elev_atl06[subset2], linewidth=2)\n",
    "ax[0].set_ylim(ylims) # Ideal test case is [204.5, 223]\n",
    "ax[1].set_ylim(ylims) # Secon test case is [217.5, 226.5]\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plots demonstrate the differences between ATL03 and ATL06 over water. Note that these plots are based on high confidence photons from all surface types. If we only look at the photons classified as land ice..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting\n",
    "#subset1 = ( (lat_atl03>=-72.925) & (lat_atl03<=-72.875))\n",
    "#subset2 = ( (lat_atl06 >= -72.925) & (lat_atl06 <= -72.875) )\n",
    "\n",
    "%matplotlib widget\n",
    "f1,ax = plt.subplots(1, 2,figsize=(10,6))\n",
    "\n",
    "ax[0].plot(lat_atl03[subset1], elev_atl03_liex[subset1], '.', markersize=1)\n",
    "ax[1].plot(lat_atl06[subset2],elev_atl06[subset2], linewidth=2)\n",
    "ax[0].set_ylim(ylims)\n",
    "ax[1].set_ylim(ylims)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bed is incomplete! This implies that some of the bed photons are classified differently."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting\n",
    "#subset1 = ( (lat_atl03>=-72.925) & (lat_atl03<=-72.875))\n",
    "#subset2 = ( (lat_atl06 >= -72.925) & (lat_atl06 <= -72.875) )\n",
    "\n",
    "elev_atl03_liex_low[ph_class[:,3]!=1] = np.nan\n",
    "\n",
    "%matplotlib widget\n",
    "f1,ax = plt.subplots(1, 2,figsize=(10,6))\n",
    "\n",
    "ax[0].plot(lat_atl03[subset1], elev_atl03_liex[subset1], '.', markersize=1, label='High Confidence')\n",
    "ax[0].plot(lat_atl03[subset1], elev_atl03_liex_low[subset1], '.', markersize=1, label='Low Confidence')\n",
    "ax[1].plot(lat_atl06[subset2],elev_atl06[subset2], linewidth=2)\n",
    "ax[0].set_ylim(ylims)\n",
    "ax[0].legend()\n",
    "ax[1].set_ylim(ylims)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The land ice classification assigns the missing photons as low confidence! That's why it's missing in ATL06. Now, let's examine the background counting rates for both data sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with h5py.File('/home/jovyan/ATL06/ATL06_20190102184312_00810210_001_01.h5') as f:\n",
    "    elev_atl06 = f['/gt2l/land_ice_segments/h_li'][:]\n",
    "    lat_atl06 = f['/gt2l/land_ice_segments/latitude'][:]\n",
    "    background_atl06 = f['/gt2l/land_ice_segments/geophysical/bckgrd'][:]\n",
    "    bsnow_od_atl06 = f['/gt2l/land_ice_segments/geophysical/bsnow_od'][:]\n",
    "    f.close()\n",
    "\n",
    "\n",
    "background_atl06[ (background_atl06 > 10000000)] = np.nan\n",
    "%matplotlib inline\n",
    "f1, ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "ax.plot(lat_atl03[subset1], elev_atl03[subset1], 'b.', markersize=1)\n",
    "ax.set_xlabel('latitude')\n",
    "ax.set_ylabel('elevation [m]', color='b')\n",
    "ax.tick_params(axis='y', labelcolor='b')\n",
    "ax.set_ylim(ylims)\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(lat_atl06[subset2], bsnow_od_atl06[subset2], 'orange')\n",
    "ax2.set_ylabel('Blowing Snow Optical Depth', color='orange')\n",
    "ax2.tick_params(axis='y', labelcolor='orange')\n",
    "\n",
    "#testharder\n",
    "#f1.tight_layout\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exciting! It looks like background rates decrease significantly over melt ponds. This calls for more analysis...\n",
    "\n",
    "Celebrate success!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "with h5py.File('/home/jovyan/ATL06/ATL06_20190102184312_00810210_001_01.h5') as f:\n",
    "    snr = f['/gt2l/land_ice_segments/fit_statistics/snr'][:]\n",
    "    across_track_slope = f['/gt2l/land_ice_segments/fit_statistics/dh_fit_dy'][:]\n",
    "    f.close()\n",
    "\n",
    "across_track_slope[ (across_track_slope > 10000)] = np.nan\n",
    "    \n",
    "f1, ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "ax.plot(lat_atl03[subset1], elev_atl03[subset1], 'b.', markersize=1)\n",
    "ax.set_xlabel('latitude')\n",
    "ax.set_ylabel('elevation [m]', color='b')\n",
    "ax.tick_params(axis='y', labelcolor='b')\n",
    "ax.set_ylim(ylims)\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(lat_atl06[subset2], across_track_slope[subset2], 'orange')\n",
    "ax2.set_ylabel('SNR', color='orange')\n",
    "ax2.tick_params(axis='y', labelcolor='orange')\n",
    "\n",
    "#testharder\n",
    "#f1.tight_layout\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the same plot as above, but it examines the signal-to-noise ratio as derived in ATL06. The SNR is approximated as:\n",
    "\n",
    "$SNR = \\frac{N_{tot} - N_{BG}}{N_{BG}}$,\n",
    "\n",
    "where $N_{tot}$ is the total number of received photons and $N_{BG}$ is the total number of background photons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
